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Anticipation of mid-span deflection at ultimate load for concrete beams strengthened by FRP bars using artificial neural networks
Author
Source
Journal of University of Babylon for Engineering Sciences
Issue
Vol. 26, Issue 5 (31 May. 2018), pp.9-19, 11 p.
Publisher
Publication Date
2018-05-31
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Abstract EN
A mid-span deflection of concrete beam strengthened by FRP bars has been anticipated.
A case of simply supported beam loaded by two point loads has been simulated by using artificial neural networks (ANNs) which are involved in MATLAB package, version 9.0.0.341360 (R2016a).
The proposed model presupposes 60 beam specimens to collect the required data for the neural pattern.
A set of 8 input variables was selected to construct the proposed neural pattern; these are, beam dimensions, concrete specifications, and FRP properties.
While the mid-span deflection at the ultimate load, will be the output inconstant.
The use of 9 nodes in the hidden layer is active in predicting the mid-span deflection.
A comparison between the gained results and the past experimental data shows that the proposed neural pattern gives a reasonable anticipation with an overall error of 8.2 %.
American Psychological Association (APA)
Abbas, Abd al-Nasir Muhammad. 2018. Anticipation of mid-span deflection at ultimate load for concrete beams strengthened by FRP bars using artificial neural networks. Journal of University of Babylon for Engineering Sciences،Vol. 26, no. 5, pp.9-19.
https://search.emarefa.net/detail/BIM-923584
Modern Language Association (MLA)
Abbas, Abd al-Nasir Muhammad. Anticipation of mid-span deflection at ultimate load for concrete beams strengthened by FRP bars using artificial neural networks. Journal of University of Babylon for Engineering Sciences Vol. 26, no. 5 (2018), pp.9-19.
https://search.emarefa.net/detail/BIM-923584
American Medical Association (AMA)
Abbas, Abd al-Nasir Muhammad. Anticipation of mid-span deflection at ultimate load for concrete beams strengthened by FRP bars using artificial neural networks. Journal of University of Babylon for Engineering Sciences. 2018. Vol. 26, no. 5, pp.9-19.
https://search.emarefa.net/detail/BIM-923584
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 16-19
Record ID
BIM-923584